Convolutional neural network for automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon acquisitions
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Publication date
2022
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01A - Journal article
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Scientific Reports
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Volume
12
Issue / Number
22059
Pages / Duration
1-11
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Nature
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Abstract
We evaluated the effectiveness of automated segmentation of the liver and its vessels with a convolutional neural network on non-contrast T1 vibe Dixon acquisitions. A dataset of non-contrast T1 vibe Dixon liver magnetic resonance images was labelled slice-by-slice for the outer liver border, portal, and hepatic veins by an expert. A 3D U-Net convolutional neural network was trained with different combinations of Dixon in-phase, opposed-phase, water, and fat reconstructions. The neural network trained with the single-modal in-phase reconstructions achieved a high performance for liver parenchyma (Dice 0.936 ± 0.02), portal veins (0.634 ± 0.09), and hepatic veins (0.532 ± 0.12) segmentation. No benefit of using multi-modal input was observed (p = 1.0 for all experiments), combining in-phase, opposed-phase, fat, and water reconstruction. Accuracy for differentiation between portal and hepatic veins was 99% for portal veins and 97% for hepatic veins in the central region and slightly lower in the peripheral region (91% for portal veins, 80% for hepatic veins). In conclusion, deep learning-based automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon was highly effective. The single-modal in-phase input achieved the best performance in segmentation and differentiation between portal and hepatic veins.
Keywords
Biomedical engineering, Computer science, Liver, Liver fibrosis
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ISBN
ISSN
2045-2322
Language
English
Created during FHNW affiliation
No
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Publication status
Published
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Peer review of the complete publication
Open access category
Gold
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Citation
Zbinden, L., Catucci, D., Suter, Y., Berzigotti, A., Ebner, L., Christe, A., Obmann, V. C., Sznitman, R., & Huber, A. T. (2022). Convolutional neural network for automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon acquisitions. Scientific Reports, 12(22059), 1–11. https://doi.org/10.1038/s41598-022-26328-2